Hypothesis Test
Hypothesis testing is a statistical method that is used to determine whether there is enough evidence in a sample of data to infer that a certain condition is true for the entire population. The process involves formulating two hypotheses: the null hypothesis, which assumes that there is no significant difference between the sample and the population, and the alternative hypothesis, which assumes that there is a significant difference. The test then calculates a p-value, which is the probability of obtaining a result as extreme as the one observed, assuming that the null hypothesis is true. If the p-value is less than a predetermined significance level, typically 0.05, the null hypothesis is rejected in favor of the alternative hypothesis. Hypothesis testing is widely used in data science to make decisions based on data and to draw conclusions about populations based on samples.
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